{"id":"W4401108292","doi":"10.1007/s43657-024-00157-x","title":"Associations of Plasma Lipidomic Profiles with Uric Acid and Hyperuricemia Risk in Middle-Aged and Elderly Chinese","year":2024,"lang":"en","type":"article","venue":"Phenomics","topic":"Gout, Hyperuricemia, Uric Acid","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Ministry of Science and Technology of the People's Republic of China; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Lipidomics; Hyperuricemia; Internal medicine; Blood lipids; Endocrinology; Lipid metabolism; Population; Diacylglycerol kinase; Uric acid; Lipidome; Lipogenesis; Chemistry; Medicine; Biology; Cholesterol; Biochemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000328039,0.0002696503,0.0003894642,0.0006926341,0.0003544053,0.0004625899,0.0001829221,0.0002994947,0.0009499852],"category_scores_gemma":[0.0006755537,0.0001616025,0.0004047231,0.001082593,0.0001397053,0.0002525729,0.0003125658,0.0002289204,0.00008789941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002670601,"about_ca_system_score_gemma":0.000312948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01189007,"about_ca_topic_score_gemma":0.01013661,"domain_scores_codex":[0.9998418,0.00002880302,0.00001935824,0.00004252849,0.00003095233,0.00003649135],"domain_scores_gemma":[0.9997492,0.00003280635,0.00009710937,0.00002595219,0.00004280743,0.00005211331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001928206,0.00002291183,0.9952582,0.00003035666,0.0002045602,0.00008986425,0.00008835401,0.00004998523,0.001213586,0.00003240646,0.00007887583,0.002738029],"study_design_scores_gemma":[0.000004385151,0.00003886738,0.9990767,0.000005060859,0.00008458683,0.00006648293,0.00009164422,0.0003231604,0.000126189,0.00005796893,0.0001217329,0.000003240921],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984984,0.000743945,0.0001071841,0.00005265702,0.00000538833,0.000005710403,0.0002894024,0.000003908466,0.0002934657],"genre_scores_gemma":[0.9994104,0.0001984114,0.00006585877,0.00002174886,0.000009072493,0.000004269963,0.0001628041,7.413773e-7,0.000126786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01189007,"threshold_uncertainty_score":0.02364171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361772979963522,"score_gpt":0.2391020791031158,"score_spread":0.2254843493034805,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}